{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 上海二手房数据分析\n",
    "## 一、根据上海的部分二手房信息，从多角度进行观察和分析房价与哪些因素有关以及房屋不同状况所占比例\n",
    "## 二、先对数据进行预处理、构造预测房价的模型、并输入参数对房价进行预测\n",
    "备注：数据来源CSDN下载。上海链家二手房.csv.因文件读入问题，改名为shanghai.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、导入数据 对数据进行一些简单的预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入需要用到的包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "sns.set_style({'font.sans-serif':['simhei','Arial']})\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>house_img</th>\n",
       "      <th>s_cate_href</th>\n",
       "      <th>house_desc</th>\n",
       "      <th>zone_href</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>house_href</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>卧室带阳台，卧室全南，地铁房，低区出入方便</td>\n",
       "      <td>http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/quyang</td>\n",
       "      <td>1室0厅|37.6平|低区/6层|朝南</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/hongkou</td>\n",
       "      <td>虹口二手房</td>\n",
       "      <td>大二小区</td>\n",
       "      <td>250</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/sh4534309.html</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>单价66489元/平</td>\n",
       "      <td>1985年建</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             house_title                                          house_img  \\\n",
       "0  卧室带阳台，卧室全南，地铁房，低区出入方便  http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...   \n",
       "\n",
       "                               s_cate_href           house_desc  \\\n",
       "0  http://sh.lianjia.com/ershoufang/quyang  1室0厅|37.6平|低区/6层|朝南   \n",
       "\n",
       "                                  zone_href district house_detail  \\\n",
       "0  http://sh.lianjia.com/ershoufang/hongkou    虹口二手房         大二小区   \n",
       "\n",
       "   house_price                                       house_href s_cate  \\\n",
       "0          250  http://sh.lianjia.com/ershoufang/sh4534309.html     曲阳   \n",
       "\n",
       "  singel_price house_time  \n",
       "0   单价66489元/平     1985年建  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shanghai=pd.read_csv('shanghai.csv')# 将已有数据导进来\n",
    "shanghai.head(n=1)#显示第一行数据 查看数据是否导入成功\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每项数据类型均为object 不方便处理，需要对一些项删除单位转换为int或者float类型\n",
    "### 有些列冗余 像house_img需要删除\n",
    "### 有些列 如何house_desc包含多种信息 需要逐个提出来单独处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73316.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>557.255415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>563.623966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>630.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        house_price\n",
       "count  73316.000000\n",
       "mean     557.255415\n",
       "std      563.623966\n",
       "min       48.000000\n",
       "25%      290.000000\n",
       "50%      400.000000\n",
       "75%      630.000000\n",
       "max    35000.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shanghai.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73316 entries, 0 to 73315\n",
      "Data columns (total 12 columns):\n",
      "house_title     73314 non-null object\n",
      "house_img       70428 non-null object\n",
      "s_cate_href     73316 non-null object\n",
      "house_desc      73316 non-null object\n",
      "zone_href       73316 non-null object\n",
      "district        73316 non-null object\n",
      "house_detail    73316 non-null object\n",
      "house_price     73316 non-null int64\n",
      "house_href      73316 non-null object\n",
      "s_cate          73316 non-null object\n",
      "singel_price    73316 non-null object\n",
      "house_time      70515 non-null object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# 检查缺失值情况\n",
    "shanghai.info()\n",
    "#np.isnan(shanghai).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shanghai.dropna(inplace=True)\n",
    "#数据处理 删除带有NAN项的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1室0厅|37.6平|低区/6层|朝南'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=shanghai.copy()\n",
    "house_desc=df['house_desc']\n",
    "house_desc[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### house_desc 中带有 室厅的信息 房子面积 楼层 朝向信息 需要分别提出来当一列 下面进行提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>house_img</th>\n",
       "      <th>s_cate_href</th>\n",
       "      <th>house_desc</th>\n",
       "      <th>zone_href</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>house_href</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>layout</th>\n",
       "      <th>area</th>\n",
       "      <th>temp</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>卧室带阳台，卧室全南，地铁房，低区出入方便</td>\n",
       "      <td>http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/quyang</td>\n",
       "      <td>1室0厅|37.6平|低区/6层|朝南</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/hongkou</td>\n",
       "      <td>虹口二手房</td>\n",
       "      <td>大二小区</td>\n",
       "      <td>250</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/sh4534309.html</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>单价66489元/平</td>\n",
       "      <td>1985年建</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>37.6平</td>\n",
       "      <td>低区/6层</td>\n",
       "      <td>低区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             house_title                                          house_img  \\\n",
       "0  卧室带阳台，卧室全南，地铁房，低区出入方便  http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...   \n",
       "\n",
       "                               s_cate_href           house_desc  \\\n",
       "0  http://sh.lianjia.com/ershoufang/quyang  1室0厅|37.6平|低区/6层|朝南   \n",
       "\n",
       "                                  zone_href district house_detail  \\\n",
       "0  http://sh.lianjia.com/ershoufang/hongkou    虹口二手房         大二小区   \n",
       "\n",
       "   house_price                                       house_href s_cate  \\\n",
       "0          250  http://sh.lianjia.com/ershoufang/sh4534309.html     曲阳   \n",
       "\n",
       "  singel_price house_time layout   area   temp floor  \n",
       "0   单价66489元/平     1985年建   1室0厅  37.6平  低区/6层    低区  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['layout']=df['house_desc'].map(lambda x:x.split('|')[0])\n",
    "df['area']=df['house_desc'].map(lambda x:x.split('|')[1])\n",
    "df['temp']=df['house_desc'].map(lambda x:x.split('|')[2])\n",
    "#df['Dirextion']=df['house_desc'].map(lambda x:x.split('|')[3])\n",
    "df['floor']=df['temp'].map(lambda x:x.split('/')[0])\n",
    "df.head(n=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一些列中带有单位 不利于后期处理 去掉单位 并把数据类型转换为float或int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>house_img</th>\n",
       "      <th>s_cate_href</th>\n",
       "      <th>house_desc</th>\n",
       "      <th>zone_href</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>house_href</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>layout</th>\n",
       "      <th>area</th>\n",
       "      <th>temp</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>卧室带阳台，卧室全南，地铁房，低区出入方便</td>\n",
       "      <td>http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/quyang</td>\n",
       "      <td>1室0厅|37.6平|低区/6层|朝南</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/hongkou</td>\n",
       "      <td>虹口</td>\n",
       "      <td>大二小区</td>\n",
       "      <td>250</td>\n",
       "      <td>http://sh.lianjia.com/ershoufang/sh4534309.html</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>66489</td>\n",
       "      <td>1985</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>37.6</td>\n",
       "      <td>低区/6层</td>\n",
       "      <td>低区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             house_title                                          house_img  \\\n",
       "0  卧室带阳台，卧室全南，地铁房，低区出入方便  http://cdn1.dooioo.com/fetch/vp/fy/gi/20170305...   \n",
       "\n",
       "                               s_cate_href           house_desc  \\\n",
       "0  http://sh.lianjia.com/ershoufang/quyang  1室0厅|37.6平|低区/6层|朝南   \n",
       "\n",
       "                                  zone_href district house_detail  \\\n",
       "0  http://sh.lianjia.com/ershoufang/hongkou       虹口         大二小区   \n",
       "\n",
       "   house_price                                       house_href s_cate  \\\n",
       "0          250  http://sh.lianjia.com/ershoufang/sh4534309.html     曲阳   \n",
       "\n",
       "  singel_price house_time layout  area   temp floor  \n",
       "0        66489       1985   1室0厅  37.6  低区/6层    低区  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['area']=df['area'].apply(lambda x:x.rstrip('平'))\n",
    "df['singel_price']=df['singel_price'].apply(lambda x:x.rstrip('元/平'))\n",
    "df['singel_price']=df['singel_price'].apply(lambda x:x.lstrip('单价'))\n",
    "df['district']=df['district'].apply(lambda x:x.rstrip('二手房'))\n",
    "df['house_time']=df['house_time'].apply(lambda x:str(x))\n",
    "df['house_time']=df['house_time'].apply(lambda x:x.rstrip('年建'))\n",
    "df.head(n=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除一些不需要用到的列 以及 house_desc、temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['house_img']\n",
    "del df['s_cate_href']\n",
    "del df['house_desc']\n",
    "del df['zone_href']\n",
    "del df['house_href']\n",
    "del df['temp']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据房子总价和房子面积 计算房子每平方米的价格\n",
    "### 从house_title 描述房子信息中提取关键词。若带有 交通便利、地铁则认为其交通方便，否则交通不便"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>layout</th>\n",
       "      <th>area</th>\n",
       "      <th>floor</th>\n",
       "      <th>trafic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>卧室带阳台，卧室全南，地铁房，低区出入方便</td>\n",
       "      <td>虹口</td>\n",
       "      <td>大二小区</td>\n",
       "      <td>250</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>66489.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>1室0厅</td>\n",
       "      <td>37.6</td>\n",
       "      <td>低区</td>\n",
       "      <td>交通便利</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             house_title district house_detail  house_price s_cate  \\\n",
       "0  卧室带阳台，卧室全南，地铁房，低区出入方便       虹口         大二小区          250     曲阳   \n",
       "\n",
       "   singel_price house_time layout  area floor trafic  \n",
       "0       66489.0       1985   1室0厅  37.6    低区   交通便利  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=1)\n",
    "df['singel_price']=df['singel_price'].apply(lambda x:float(x))\n",
    "df['area']=df['area'].apply(lambda x:float(x))\n",
    "df.head(n=1)\n",
    "#df['trafic']=df['house_title'].apply(lambda x:'交通便利' if x.find(\"交通便利\")>=0 or x.find(\"地铁\")>=0 else \"交通不便\"  )\n",
    "df.head(n=1)\n",
    "df['house_title']=df['house_title'].apply(lambda x:str(x))\n",
    "df['trafic']=df['house_title'].apply(lambda x:'交通便利' if x.find(\"交通便利\")>=0 or x.find(\"地铁\")>=0 else \"交通不便\"  )\n",
    "df.head(n=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、根据各列信息 用可视化的形式展现 房价与不同因素如地区、房子面积、所在楼层等之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SYK3utnVpwVafNkmSJElz3K+f/ZSZ7sKkuMP7PjHTXZCkGTdhWFNVpwDPAC6k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\nWI+i5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_house_count = df.groupby('district')['house_price'].count().sort_values(ascending=False).to_frame().reset_index()\n",
    "df_house_mean = df.groupby('district')['singel_price'].mean().sort_values(ascending=False).to_frame().reset_index()\n",
    "\n",
    "f, [ax1,ax2,ax3] = plt.subplots(3,1,figsize=(20,15))\n",
    "sns.barplot(x='district', y='singel_price', palette=\"Reds_d\", data=df_house_mean, ax=ax1)\n",
    "ax1.set_title('上海各大区二手房每平米单价对比',fontsize=15)\n",
    "ax1.set_xlabel('区域')\n",
    "ax1.set_ylabel('每平米单价')\n",
    "sns.countplot(df['district'], ax=ax2)\n",
    "sns.boxplot(x='district', y='house_price', data=df, ax=ax3)\n",
    "ax3.set_title('上海各大区二手房房屋总价',fontsize=15)\n",
    "ax3.set_xlabel('区域')\n",
    "ax3.set_ylabel('房屋总价')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  上面三幅图显示了 房子单价、总数量、总价与地区之间的关系。\n",
    "#### 由上面第一幅图可以看到房子单价与地区有关，其中黄浦以及静安地区房价最高。这与地区的发展水平、交通便利程度以及离市中心远近程度有关\n",
    "#### 由上面第二幅图可以直接看出不同地区的二手房数量，其中浦东最多\n",
    "#### 由上面第三幅图可以看出上海二手房房价基本在一千万上下，很少有高于两千万的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>layout</th>\n",
       "      <th>area</th>\n",
       "      <th>floor</th>\n",
       "      <th>trafic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [house_title, district, house_detail, house_price, s_cate, singel_price, house_time, layout, area, floor, trafic]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['area']<10]\n",
    "# 查看area样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uuLg1dXEKce0hfmGuRjfLYiJ23woREZns74ADwBXAfwb+GriqxrnvAD7t7v9s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\n/j8RkYiPyZ79PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1,ax2] = plt.subplots(1, 2, figsize=(15, 5))\n",
    "# 二手房的面积分布\n",
    "sns.distplot(df['area'], bins=20, ax=ax1, color='r')\n",
    "sns.kdeplot(df['area'], shade=True, ax=ax1)\n",
    "# 二手房面积和价位的关系\n",
    "sns.regplot(x='area', y='house_price', data=df, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 由从左到右第一幅图可以看出 基本二手房面积在60-200平方米之间，其中一百平方米左右的占比更大\n",
    "### 由第二幅看出，二手房总结与二手房面积基本成正比，和我们的常识吻合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BRW4cNvPp3qGYZMp9AXZ8J4/lH8X48doIL3/Jy5sNOl+YnLofyZjBHdzVkdol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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "areas=[len(df[df.area<100]),len(df[(df.area>100)&(df.area<200)]),len(df[df.area>200])]\n",
    "labels=['area<100' , '100<area<200','area>200']\n",
    "plt.pie(areas,labels= labels,autopct='%0f%%',shadow=True)\n",
    "plt.show()\n",
    "# 绘制饼图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将面积划分为三个档次，面积大于200、面积小与100、面积在一百到两百之间 三者的占比情况可以发现 百分之六十九的房子面积在一百平方米一下，高于一百大于200的只有百分之二十五而面积大于两百的只有百分之四"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>layout</th>\n",
       "      <th>area</th>\n",
       "      <th>floor</th>\n",
       "      <th>trafic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>洪中路138号，简单装修，高清实拍，链家好房</td>\n",
       "      <td>奉贤</td>\n",
       "      <td>洪中路138号</td>\n",
       "      <td>3500</td>\n",
       "      <td>奉城</td>\n",
       "      <td>7628.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>6室3厅</td>\n",
       "      <td>4588.00</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21867</th>\n",
       "      <td>绿城玫瑰园，上门实勘，链家真房源，链家好房</td>\n",
       "      <td>闵行</td>\n",
       "      <td>绿城玫瑰园</td>\n",
       "      <td>26000</td>\n",
       "      <td>马桥</td>\n",
       "      <td>158740.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6室4厅</td>\n",
       "      <td>1637.89</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22894</th>\n",
       "      <td>品质装修，满五年，实地看房，上门实拍</td>\n",
       "      <td>闵行</td>\n",
       "      <td>绿城玫瑰园</td>\n",
       "      <td>15000</td>\n",
       "      <td>马桥</td>\n",
       "      <td>97816.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>6室2厅</td>\n",
       "      <td>1533.48</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35871</th>\n",
       "      <td>南北通，出行方便，拎包入住，满5年</td>\n",
       "      <td>松江</td>\n",
       "      <td>佘山高尔夫郡</td>\n",
       "      <td>9000</td>\n",
       "      <td>佘山</td>\n",
       "      <td>89982.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>5室4厅</td>\n",
       "      <td>1000.20</td>\n",
       "      <td>地上2层地下1层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37165</th>\n",
       "      <td>毛坯房，满五年，实地看房，上门实拍</td>\n",
       "      <td>松江</td>\n",
       "      <td>世茂佘山庄园</td>\n",
       "      <td>35000</td>\n",
       "      <td>佘山</td>\n",
       "      <td>191889.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>6室3厅</td>\n",
       "      <td>1823.97</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38986</th>\n",
       "      <td>世茂佘山庄园，毛坯房，房本满五年，高清实拍</td>\n",
       "      <td>松江</td>\n",
       "      <td>世茂佘山庄园</td>\n",
       "      <td>11000</td>\n",
       "      <td>佘山</td>\n",
       "      <td>104961.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>5室3厅</td>\n",
       "      <td>1048.00</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38993</th>\n",
       "      <td>世茂佘山庄园，满5年，热门带看，高清摄影</td>\n",
       "      <td>松江</td>\n",
       "      <td>世茂佘山庄园</td>\n",
       "      <td>9000</td>\n",
       "      <td>佘山</td>\n",
       "      <td>85714.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>5室3厅</td>\n",
       "      <td>1050.00</td>\n",
       "      <td>地上4层</td>\n",
       "      <td>交通不便</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40015</th>\n",
       "      <td>稀缺南北通，地铁直达，经典装修，满五减税</td>\n",
       "      <td>松江</td>\n",
       "      <td>佘山月湖山庄</td>\n",
       "      <td>3800</td>\n",
       "      <td>佘山</td>\n",
       "      <td>33765.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>7室4厅</td>\n",
       "      <td>1125.40</td>\n",
       "      <td>地上2层</td>\n",
       "      <td>交通便利</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70695</th>\n",
       "      <td>南北通，交通便利，低楼层，装修经典</td>\n",
       "      <td>浦东</td>\n",
       "      <td>浦东星河湾</td>\n",
       "      <td>7200</td>\n",
       "      <td>北蔡</td>\n",
       "      <td>69086.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>8室3厅</td>\n",
       "      <td>1042.17</td>\n",
       "      <td>低区</td>\n",
       "      <td>交通便利</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  house_title district house_detail  house_price s_cate  \\\n",
       "2704   洪中路138号，简单装修，高清实拍，链家好房       奉贤      洪中路138号         3500     奉城   \n",
       "21867   绿城玫瑰园，上门实勘，链家真房源，链家好房       闵行        绿城玫瑰园        26000     马桥   \n",
       "22894      品质装修，满五年，实地看房，上门实拍       闵行        绿城玫瑰园        15000     马桥   \n",
       "35871       南北通，出行方便，拎包入住，满5年       松江       佘山高尔夫郡         9000     佘山   \n",
       "37165       毛坯房，满五年，实地看房，上门实拍       松江       世茂佘山庄园        35000     佘山   \n",
       "38986   世茂佘山庄园，毛坯房，房本满五年，高清实拍       松江       世茂佘山庄园        11000     佘山   \n",
       "38993    世茂佘山庄园，满5年，热门带看，高清摄影       松江       世茂佘山庄园         9000     佘山   \n",
       "40015    稀缺南北通，地铁直达，经典装修，满五减税       松江       佘山月湖山庄         3800     佘山   \n",
       "70695       南北通，交通便利，低楼层，装修经典       浦东        浦东星河湾         7200     北蔡   \n",
       "\n",
       "       singel_price house_time layout     area     floor trafic  \n",
       "2704         7628.0       2003   6室3厅  4588.00      地上2层   交通不便  \n",
       "21867      158740.0       2014   6室4厅  1637.89      地上2层   交通不便  \n",
       "22894       97816.0       2007   6室2厅  1533.48      地上2层   交通不便  \n",
       "35871       89982.0       2005   5室4厅  1000.20  地上2层地下1层   交通不便  \n",
       "37165      191889.0       2008   6室3厅  1823.97      地上2层   交通不便  \n",
       "38986      104961.0       2007   5室3厅  1048.00      地上2层   交通不便  \n",
       "38993       85714.0       2006   5室3厅  1050.00      地上4层   交通不便  \n",
       "40015       33765.0       2004   7室4厅  1125.40      地上2层   交通便利  \n",
       "70695       69086.0       2010   8室3厅  1042.17        低区   交通便利  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['area']>1000]\n",
    "# 查看size>1000的样本 发现只有一个是大于1000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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85tYIsKyq5jfPTwGGq+rKTXz/PGAewG/N2rHFXyZJkiRJkqSN6Sx0SjILuAJ4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\ndJIkSZIkSVLn/g832GGr5+70RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JkiSpGMMmSZIkSZIkFTPoYVNEjI+IzkJrdUZElFhLkiRJkiRJA1fL1+giYhxwBvCLzHyt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\nayw2SZIkSZIkqTUWmyRJkiRJktSa/wcbTgdYET9lugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1= plt.subplots(figsize=(20,20))\n",
    "sns.countplot(y='layout', data=df, ax=ax1)\n",
    "ax1.set_title('房屋户型',fontsize=15)\n",
    "ax1.set_xlabel('数量')\n",
    "ax1.set_ylabel('户型')\n",
    "f, ax2= plt.subplots(figsize=(20,20))\n",
    "sns.barplot(y='layout', x='house_price', data=df, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 上述两幅图显示了 不同户型的数量和价格\n",
    "#### 由第一幅图看出2室1厅最多 2室2厅 3室2厅也较多 是主流的户型选择 \n",
    "#### 由第二幅看出 室和厅的数量增加随之价格也增加，但是室和厅之间的比例要适合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jnX8vs27+z2r7jmQ9z+wuacMJUFVjvQc8A4wAjL++E527eKO9QHFZmgJSSp7/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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1=0\n",
    "a2=0\n",
    "for x in df['trafic']:\n",
    "    \n",
    "    if x=='交通便利':\n",
    "        a1=a1+1\n",
    "    else:\n",
    "        a2=a2+1\n",
    "sizes=[a1,a2]\n",
    "labels=['交通便利' , '交通不便']\n",
    "plt.pie(sizes,labels= labels,autopct='%0f%%',shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 上述图显示了上海二手房交通不便利情况。其中百分之六十一为交通不便，百分之三十八为交通不便。由于交通便利情况仅仅是根据对房屋的描述情况提取出来的，实际上 交通便利的占比会更高些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pkm/MBEOXZWadplkKfHD1Ei7BtUx3v6aqTsyw9tJfd/cf7sTL9hhfu8XAJsOtYAdnuI1r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\nJv8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1,ax2] = plt.subplots(1, 2, figsize=(20, 10))\n",
    "sns.countplot(df['trafic'], ax=ax1)\n",
    "ax1.set_title('交通是否便利数量对比',fontsize=15)\n",
    "ax1.set_xlabel('交通是否便利')\n",
    "ax1.set_ylabel('数量')\n",
    "sns.barplot(x='trafic', y='house_price', data=df, ax=ax2)\n",
    "ax2.set_title('交通是否便利房价对比',fontsize=15)\n",
    "ax2.set_xlabel('交通是否便利')\n",
    "ax2.set_ylabel('总价')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 左边那幅图显示了交通便利以及不便的二手房数量，这与我们刚才的饼图信息一致\n",
    "### 右边那幅图显示了交通便利与否与房价的关系。交通便利的房子价格更高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h7JzPjXfN81h4VY8Lx/t+MslhYwicbP736rW6+8vjY76d5Opj+8zXq3Bpzz6T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\nAABMJlwCAAAAYDLhEgAAAACT/f+moh4ZpZiJ+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yh/27Avv0nkl+sLv/uaruMW7ro5njufBy5tzdFyS5YGx/QYb8k/X/Xn1BkvOT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\nYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1= plt.subplots(figsize=(20,5))\n",
    "sns.countplot(x='floor', data=df, ax=ax1)\n",
    "ax1.set_title('楼层',fontsize=15)\n",
    "ax1.set_xlabel('楼层数')\n",
    "ax1.set_ylabel('数量')\n",
    "f, ax2 = plt.subplots(figsize=(20, 5))\n",
    "sns.barplot(x='floor', y='house_price', data=df, ax=ax2)\n",
    "ax2.set_title('楼层',fontsize=15)\n",
    "ax2.set_xlabel('楼层数')\n",
    "ax2.set_ylabel('总价')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 楼层（地区、高区、中区、地下几层）与数量、房价的关系。高区、中区、低区居多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、根据已有数据建立简单的上海二手房房间预测模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对数据再次进行简单的预处理 把户型这列拆成室和厅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shuifan\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: currently extract(expand=None) means expand=False (return Index/Series/DataFrame) but in a future version of pandas this will be changed to expand=True (return DataFrame)\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_title</th>\n",
       "      <th>district</th>\n",
       "      <th>house_detail</th>\n",
       "      <th>house_price</th>\n",
       "      <th>s_cate</th>\n",
       "      <th>singel_price</th>\n",
       "      <th>house_time</th>\n",
       "      <th>area</th>\n",
       "      <th>floor</th>\n",
       "      <th>trafic</th>\n",
       "      <th>室</th>\n",
       "      <th>厅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>卧室带阳台，卧室全南，地铁房，低区出入方便</td>\n",
       "      <td>虹口</td>\n",
       "      <td>大二小区</td>\n",
       "      <td>250</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>66489.0</td>\n",
       "      <td>1985</td>\n",
       "      <td>37.60</td>\n",
       "      <td>低区</td>\n",
       "      <td>交通便利</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>厨卫全明，卧室带阳台，交通方便，采光好</td>\n",
       "      <td>虹口</td>\n",
       "      <td>西南小区</td>\n",
       "      <td>360</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>69916.0</td>\n",
       "      <td>1982</td>\n",
       "      <td>51.49</td>\n",
       "      <td>高区</td>\n",
       "      <td>交通不便</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>卧室带阳台，卧室全南，交通方便，高区景观好</td>\n",
       "      <td>虹口</td>\n",
       "      <td>西南小区</td>\n",
       "      <td>345</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>77720.0</td>\n",
       "      <td>1982</td>\n",
       "      <td>44.39</td>\n",
       "      <td>高区</td>\n",
       "      <td>交通不便</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>厨卫全明，卧室带阳台，地铁房，上下楼方便</td>\n",
       "      <td>虹口</td>\n",
       "      <td>曲一花苑</td>\n",
       "      <td>445</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>78358.0</td>\n",
       "      <td>1983</td>\n",
       "      <td>56.79</td>\n",
       "      <td>低区</td>\n",
       "      <td>交通便利</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>稀罕南北通，地铁沿线，采光无遮挡，拎包入住</td>\n",
       "      <td>虹口</td>\n",
       "      <td>东体小区</td>\n",
       "      <td>320</td>\n",
       "      <td>曲阳</td>\n",
       "      <td>76812.0</td>\n",
       "      <td>1983</td>\n",
       "      <td>41.66</td>\n",
       "      <td>高区</td>\n",
       "      <td>交通便利</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             house_title district house_detail  house_price s_cate  \\\n",
       "0  卧室带阳台，卧室全南，地铁房，低区出入方便       虹口         大二小区          250     曲阳   \n",
       "1    厨卫全明，卧室带阳台，交通方便，采光好       虹口         西南小区          360     曲阳   \n",
       "2  卧室带阳台，卧室全南，交通方便，高区景观好       虹口         西南小区          345     曲阳   \n",
       "3   厨卫全明，卧室带阳台，地铁房，上下楼方便       虹口         曲一花苑          445     曲阳   \n",
       "4  稀罕南北通，地铁沿线，采光无遮挡，拎包入住       虹口         东体小区          320     曲阳   \n",
       "\n",
       "   singel_price house_time   area floor trafic    室    厅  \n",
       "0       66489.0       1985  37.60    低区   交通便利  1.0  0.0  \n",
       "1       69916.0       1982  51.49    高区   交通不便  2.0  1.0  \n",
       "2       77720.0       1982  44.39    高区   交通不便  2.0  1.0  \n",
       "3       78358.0       1983  56.79    低区   交通便利  2.0  1.0  \n",
       "4       76812.0       1983  41.66    高区   交通便利  2.0  1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['室','厅']] = df['layout'].str.extract(r'(\\d+)室(\\d+)厅')\n",
    "df['室'] = df['室'].astype(float)\n",
    "df['厅'] = df['厅'].astype(float)\n",
    "#data['卫'] = data['卫'].astype(float)\n",
    "del df['layout']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 67753 entries, 0 to 73304\n",
      "Data columns (total 12 columns):\n",
      "house_title     67753 non-null object\n",
      "district        67753 non-null object\n",
      "house_detail    67753 non-null object\n",
      "house_price     67753 non-null int64\n",
      "s_cate          67753 non-null object\n",
      "singel_price    67753 non-null float64\n",
      "house_time      67753 non-null object\n",
      "area            67753 non-null float64\n",
      "floor           67753 non-null object\n",
      "trafic          67753 non-null object\n",
      "室               67753 non-null float64\n",
      "厅               67753 non-null float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['house_title', 'district', 'house_detail', 'house_price', 's_cate',\n",
       "       'singel_price', 'house_time', 'area', 'floor', 'trafic', '室', '厅'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除不需要用到的信息如房子的基本信息描述 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['house_title']\n",
    "del df['house_detail']\n",
    "del df['s_cate']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-61.47368693] [[6.41045431]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear = LinearRegression()\n",
    "area=df['area']\n",
    "price=df['house_price']\n",
    "area = np.array(area).reshape(-1,1) # 这里需要注意新版的sklearn需要将数据转换为矩阵才能进行计算\n",
    "price = np.array(price).reshape(-1,1)\n",
    "# 训练模型\n",
    "model = linear.fit(area,price)\n",
    "# 打印截距和回归系数\n",
    "\n",
    "print(model.intercept_, model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hgw0AAFJj9WovOL/1lr9+993SaaeFsyYgDQjYAAAgWPfe2zRA9+/vjYHstls4awLSiBER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\nAAAAAkTABgAAAAL0/wHz8EqMxkeM3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_p = model.predict(area)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.scatter(area,price)\n",
    "plt.plot(area,linear_p,'red')\n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 上面用线性回归模型对房价进行简单的预测 红色的代表预测房价，而蓝色点代表真实值。可以看出在面积小于1000时真实值紧密分布在预测值两旁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### 在训练模型时对str的处理不方便。因此将18个地区分别用0-17个数字表示。用 1 表示 交通便利 用 0 表示交通不便。另外将成立房子建立年份转换为floata类型."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d=['宝山','奉贤','虹口','黄浦','嘉定','静安','闵行','浦东',\n",
    "  '普陀','青浦','松江','徐汇','杨浦','闸北','长宁','金山','崇明','上海周边']\n",
    "df['district']=df['district'].apply(lambda x:d.index(x))\n",
    "df['house_time']=df['house_time'].apply(lambda x:float(x))\n",
    "df['trafic']=df['trafic'].apply(lambda x:int(1) if x=='交通便利' else int(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>house_time</th>\n",
       "      <th>area</th>\n",
       "      <th>室</th>\n",
       "      <th>厅</th>\n",
       "      <th>trafic</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>37.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>51.49</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   house_time   area    室    厅  trafic  district\n",
       "0      1985.0  37.60  1.0  0.0       1         2\n",
       "1      1982.0  51.49  2.0  1.0       0         2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#df.drop([len(df)-1],inplace=True)\n",
    "cols=['house_time','area','室','厅','trafic','district']\n",
    "X=df[cols]\n",
    "X.head(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    250\n",
       "1    360\n",
       "Name: house_price, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df['house_price']\n",
    "Y.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用了scikit-learn和线性回归算法做一个房价预测模型。房价特征有6个。分别使用一阶多项式线性回归，二阶多项式线性回归，和三阶多项式线性回归生成模型并作了性能比较。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用一阶多项式拟合结果\n",
      "elaspe:0.039540;train_score:0.622180;cv_score:0.690372\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "##将数据分为训练集和测试集,分20%作为测试集\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=3)\n",
    "#训练模型\n",
    "import time \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "result=['一阶多项式拟合结果','二阶多项式拟合结果','多项式拟合结果']\n",
    "Train_score=[]\n",
    "Cv_score=[]\n",
    "#定义多项式模型函数\n",
    "def polynomial_model(degree=1):\n",
    "    polynomial_features=PolynomialFeatures(degree=degree,include_bias=False)\n",
    "    linear_regression=LinearRegression(normalize=True)  #归一化数据进行训练，克加快算法收敛速度\n",
    "    pipeline=Pipeline([(\"polynomial_feature\",polynomial_features),(\"linear_regression\",linear_regression)])\n",
    "    return pipeline\n",
    "\n",
    "model=polynomial_model(degree=1)  #使用二阶多项式模型拟合\n",
    "\n",
    "start=time.clock()\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "#模型预测与评估\n",
    "train_score=model.score(X_train,Y_train)\n",
    "cv_score=model.score(X_test,Y_test)\n",
    "print('使用一阶多项式拟合结果')\n",
    "print('elaspe:{0:.6f};train_score:{1:0.6f};cv_score:{2:.6f}'.format(time.clock()-start,train_score,cv_score))\n",
    "Train_score.append(train_score)\n",
    "Cv_score.append(cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用二阶多项式拟合结果\n",
      "elaspe:0.207715;train_score:0.687070;cv_score:0.719131\n"
     ]
    }
   ],
   "source": [
    "model=polynomial_model(degree=2)  #使用二阶多项式模型拟合\n",
    "\n",
    "start=time.clock()\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "#模型预测与评估\n",
    "train_score=model.score(X_train,Y_train)\n",
    "cv_score=model.score(X_test,Y_test)\n",
    "print('使用二阶多项式拟合结果')\n",
    "print('elaspe:{0:.6f};train_score:{1:0.6f};cv_score:{2:.6f}'.format(time.clock()-start,train_score,cv_score))\n",
    "Train_score.append(train_score)\n",
    "Cv_score.append(cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用阶多项式拟合结果\n",
      "elaspe:0.967086;train_score:0.754008;cv_score:0.698847\n"
     ]
    }
   ],
   "source": [
    "model=polynomial_model(degree=3)  #使用二阶多项式模型拟合\n",
    "\n",
    "start=time.clock()\n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "#模型预测与评估\n",
    "train_score=model.score(X_train,Y_train)\n",
    "cv_score=model.score(X_test,Y_test)\n",
    "print('使用阶多项式拟合结果')\n",
    "print('elaspe:{0:.6f};train_score:{1:0.6f};cv_score:{2:.6f}'.format(time.clock()-start,train_score,cv_score))\n",
    "Train_score.append(train_score)\n",
    "Cv_score.append(cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYEJoBAAAAMCE0AgAAACACaERAAAAABNCIwAAAAAmhEYAAAAATAiNAAAAAJgQ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\nyUgjAICN4YgkV47HVyc5ZqW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DMkyD/7EMRyuPzDDN/vUZjuReleTzGc47UeNr1id539ju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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Train_score\n",
    "#Cv_score\n",
    "result=np.array(result)\n",
    "Train_score=np.array(Train_score)\n",
    "Cv_score=np.array(Cv_score)\n",
    "f, [ax1,ax2] = plt.subplots(1, 2, figsize=(20, 10))\n",
    "#ax1.bar(Train_score,result)\n",
    "ax1.bar(range(len(Train_score)), Train_score,color='rgb',tick_label=result)  \n",
    "ax1.set_title('各模型train_score对比',fontsize=15)\n",
    "ax1.set_xlabel('模型种类')\n",
    "ax1.set_ylabel('train_score')\n",
    "ax2.bar(range(len(Cv_score)),Cv_score,color='rgb',tick_label=result)        \n",
    "ax2.set_title('各模型cv_score对比',fontsize=15)\n",
    "ax2.set_xlabel('模型总类')\n",
    "ax2.set_ylabel('cv_score')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 由上图可以看出 随着阶次上升模型训练集准确率显著上升，测试集准确率缓慢上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义学习曲线函数\n",
    "from sklearn.model_selection import learning_curve\n",
    "import numpy as np\n",
    "\n",
    "def plot_learning_curve(plt, estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    \"\"\"\n",
    "    Generate a simple plot of the test and training learning curve.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "        An object of that type which is cloned for each validation.\n",
    "\n",
    "    title : string\n",
    "        Title for the chart.\n",
    "\n",
    "    X : 训练集的特征\n",
    "        array-like, shape (n_samples, n_features)\n",
    "        Training vector, where n_samples is the number of samples and\n",
    "        n_features is the number of features.\n",
    "\n",
    "    y : 训练集的标签\n",
    "        array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "        Target relative to X for classification or regression;\n",
    "        None for unsupervised learning.\n",
    "\n",
    "    ylim : tuple, shape (ymin, ymax), optional\n",
    "        Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "    cv : 交叉验证结果\n",
    "        int, cross-validation generator or an iterable, optional\n",
    "        Determines the cross-validation splitting strategy.\n",
    "        Possible inputs for cv are:\n",
    "          - None, to use the default 3-fold cross-validation,\n",
    "          - integer, to specify the number of folds.\n",
    "          - An object to be used as a cross-validation generator.\n",
    "          - An iterable yielding train/test splits.\n",
    "\n",
    "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
    "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
    "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
    "\n",
    "        Refer :ref:`User Guide <cross_validation>` for the various\n",
    "        cross-validators that can be used here.\n",
    "\n",
    "    n_jobs : integer, optional\n",
    "        Number of jobs to run in parallel (default 1).\n",
    "    \"\"\"\n",
    "    plt.title(title)   #画出标题\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")  #横轴标签\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)  #均值\n",
    "    train_scores_std = np.std(train_scores, axis=1)    #方差\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)   \n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")                #红色区域是准确性平均值的方差空间\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o--', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elaspe:3.023455\n",
      "elaspe:9.542797\n",
      "elaspe:37.255698\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 3600x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ySLlTyL64KtT+xWRfZBR9D+TKLC3yPDQDPyrShm90873RCOzTSR1TWsut5PPV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\ncOFC9txzT55++mlOO+00Hn/8cebOncu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NdYqIiIiISBYyGEiZ/y2pD5ivU11dSfl2nvJ1\nOtLy9Q/f/2CRr8e+O5YWL7d4IvN1TuecAERHRfP9d99neb6uXr06N27cICI8gjGjx1jk640bNrLk\nxyWAdb7+YOIHODg4sGvXLpo3bc7+3/ab8/WUj6fw9ZdfA/DGm29Y5OuPp3wMwJrVa2gT0IbIQ5Ek\nJSUReSiS1q1a8/O6nwGYOm2qXfL1uPfHATDnqzm82e9Ni3w9ZPAQtm3dZqqz/xuZ2peIiIiIiGSA\nMnaWUsa2poytjC1PFg3YFhERkezj4wNBQfc/aNvV1dTPxydr6noMDBkyBIDw8HDKli2Lk5MTlStX\nZvLkyZQoUYIqVaoAcPLkyews8z9VsWJFevXqRUJCAj179qRgwYI4OTmRJ08eihQpgo+PDwMGDOD3\n33+3y/5KlizJ+PHjAZgyZQply5YlR44c5MqVi+bNm/PXX39RoEABhg8fbtGvYcOGzJ07l5w5c7J1\n61Zq166Ns7MzRYsWZcyYMRiNRjp06MDYsWMt+nXo0IGZM2fi6OjIunXrqFatGs7OzlSrVo3169fj\n6OjI559/Tps2bexyfP369WPkyJGA6Q7lypUrkyNHDooXL85nn30GwIgRI+jUqZNd9iciIiIiIulL\n9alB8vKl931BOdXV1dTPp0YWVfboG/T2IAAiwiOoXKEyLs4uVPWqyrSp03Av4Y53FW8AYk/GZmOV\n/62KFSvSo2cPEhIS6Nu7L8WKFMPF2YUCbgVwL+ZOnVp1GDRwkF3z9bj3TBdcA6cFUrlCZVxzuZLP\nNR8B/gHmfD1k6BCLfg0aNuDLr78kZ86cbNu2jReff5G8LnkpWbwk7417D6PRSLv27Rg9ZrRFv3bt\n2xH4SSCOjo6s/3k9z/o8S16XvDzr8ywbN2zE0dGRmbNmEtA6wC7H99rrrzFs+DAAvp33LVW9quKa\ny5UyJcvwxeemm7aHDhtKh44d7LI/ERERERG5O2XsrKOMbU0ZWxlbniwasC0iIiLZq3FjCAkBL6+M\ntff2NrVv3Dhr63rEjRw5khUrVuDn50fBggVxdnbG29ub999/n7CwMJo1awbAggULuHHjRjZX+9/x\n9/cnd+7cuLu7U65cOXLnzk18fDyXLl0iIiKCL774gpo1axIVFWWX/Y0dO5bNmzfTvn17SpQogZOT\nE3nz5sXLy4tRo0YRGRlJpUqVrPr17duXiIgI+vTpg7u7O05OTjz11FM0atSIJUuWsHTpUpycnKz6\nvf322/z222907dqVYsWK4eTkRLFixejatSthYWF2n+168uTJbN++nfbt21O0aFEcHR0pWLAg/v7+\n/PLLL0yZMsWu+xMRERERkfSlNm5E8tZfMvzoZqO3F8lbfyG1caMsruzRNmzEMJYsW4Kvr685X3t5\nezF23Fj27ttLkyZNAFi8aPETla9btGxhztdly5W1yNcHIg7w9Zdf83zt54mOirbL/kaPGc36jetp\n266tRb729PJk+IjhhB0Is5mve/fpzb79++jVu5dFvm7QsAHf//g9P/z0g818PXDQQHbv3U3nLp0t\n8nXnLp3Zu28vb/Z/0y7HlWbSx5PY8usW2rZra5GvW7RswcbgjXw0+SO77k9ERERERO5OGTtrKGPb\npoytjC1PDsOT9AiBx5HBYHAHTgOcPn0ad3f3bK5I0nPr1i2Cg4MBaNKkCblz587miuRxo3NMHsSx\nY8dITk7GycmJChUq3LWt0WgkLi4OgHz58pkfY2M3qamwfTvMnm2aPTsl5Z91Tk7Qpg307w++vnqE\n1GMqq8+xRYsW0atXL4YPH87kyZMttp+QkEBsbCyvvPIKu3bt4u2332bmzJl23b9kvyx/H5P7+n8l\nzZkzZyhRokTaP0ukpqaeybICRe7hYczYt5JuceX2FS7evIhrDldccz7YozgfN4m3Ezm4+yAA1V6o\nRs5cObO5Innc6ByTB/H3mb/BCDly5KBMuTJ3bZtqTOXWzVsA5HbNjcHBjjk3NRXD9hAcv/wKw6rV\nGO7I16lOThhbB2B8ox+pvvWVrx9jWXmOfbf4O17p8wrvDH2HSR9Pspmv33jtDXbv3s1bg95i+ifT\n7bZveXhk6fvYI8CYaiTFmEIOxxw4GhxxdHC06/aVr+VR9zDma4Crt69y9fZV/r71N4VcCuHkYD2g\n6Emk/CNZTeeY3K/7ydegjC1ZTxlbstKTnq8BUoym91ZHB0dyOOTAYOf304cpYyuBiIiIyMPBYAA/\nP9NXXBycPQvXr0PevFC8OOTLl90VyiNu6dKlAJw8eZKYmBjKly+Ps7MzACkpKZw7d46//voLMA3m\nFREREREReSQZDKT6+ZLs52vO14brN0jNm0f5Wuxi+bLlAMTGxnIk5gjlypezyNfnz53nr8v/n6/z\n6nwTEREREZFHmDK2ZDFlbJEniwZsi4iIyMMnXz6FW7G7nj17sn79epYvX87y5abgmyNHDgCSkpLM\n7cqXL8+AAQOypUYRERERERG7+v98redsij1179GdjRs2snLFSlauWAnYztflypfjjf5vZEuNIiIi\nIiIidqeMLVlAGVvkyaIB2yIiIiLyROjYsSNlypRhzpw57N27l9OnT3Pjxg2cnZ1xd3enSpUqBAQE\n0KVLF3Lnzp3d5YqIiIiIiIg8lNp3aE/p0qWZ9808QkNDOXP6jDlfF3cvjre3N/6t/OnUuZPytYiI\niIiIiMhdKGOLPFk0YFtEREREnhi1atWiVq1a2V2GiIiIiIiIyCPt2VrP8mytZ7O7DBEREREREZFH\nnjK2yJPDIbsLEBEREREREREREREREREREREREREREXlcacC2iIiIiIiIiIiIiIiIiIiIiIiIiIiI\nSBbRgG0RERERERERERERERERERERERERERGRLKIB2yIiIiIiIiIiIiIiIiIiIiIiIiIiIiJZRAO2\nRURERERERERERERERERERERERERERLKIBmyLiIiIiIiIiIiIiIiIiIiIiIiIiIiIZBEN2BYRERER\nERERERERERERERERERERERHJIhqwLSIiIiIiIiIiIiIiIiIiIiIiIiIiIpJFNGBbRERERERERERE\nREREREREREREREREJItowLaIiIiIiIiIiIiIiIiIiIiIiIiIiIhIFtGAbRERERERERERERERERER\nEREREREREZEsogHbIiIiIiIiIiIiIiIiIiIiIiIiIiIiIllEA7ZFRERERERERERERERERERERERE\nREREsogGbIuIiMhDJy4hjsOXDrPv7D4OXzpMXEJcdpdkV2FhYXTp0oVixYqRK1cuypUrx5AhQ7h0\n6VJ2lyYZZDAYMBgMxMbGZrjPtm3bMBgMlC5dOlP73rlzp122Y29+fn4YDAYWLFiQ3aWIiIiIiMj/\ni0uII+ZSDL+d/Y2YSzGPXb4ODwunR7celHIvRT7XfFSuWJlh7wxTvn6EODs54+zkfF/5evu27Tg7\nOVOxXMVM7XvXzl122Y69NW7QGGcnZxYtXJTdpYiIiIiIyB2UseVhp4xtTRlbHjZO2V2AiIiICEBq\nairbYrcx+7fZrDqyipTUFPM6R4MjbTza0P/Z/viVNg0KfVR9/fXXvPXWWyQnJwPg4ODAiRMnmDlz\nJkuWLGHz5s14eXllc5XypFqwYAGxsbG0bt2a6tWrZ3c5IiIiIiLyAFJTU9l+ajtf7f+KNUfXWOXr\ngMoB9KvZD99Svo90vp47Zy6DBw22yNcnT5zks1mfsXzZctZvXI+nl2c2VylPqkULF3Eq9hStAlpR\nrXq17C5HREREREQekDK2MrZkP2VseZxohm0RERHJduHnwqnyZRUaLGrAipgVFkEXICU1heWHl9Ng\nUQOqfFmF8HPh2VRp5mzatIn+/fuTnJyMv78/R48eJTk5mcjISJ599lnOnTtHu3btSExMzO5SJQu4\nuLhQqVIlypUrl92lpGvBggVMmDCBAwcO3HffkiVLUqlSJdzc3LKgMhERERERyYiIcxH4zPGh6XdN\nCToSZDNfr4xZSdPvmuIzx4eIcxHZVGnmBG8KZtBbg0hOTqZFyxZEHo4kPiGe8APh1Hy2JufOnaNT\nx07K148pFxcXKlaqSJmyZbK7lHQtXriYiR9O5ODBg/fdt0TJElSsVFH5WkREREQkmyljK2M/CZSx\nRf5bmmFbREREstXm45tps6QNN5NuZqh99KVo6s+vT1CnIBqXa5zF1dlPSkoKAwcOxGg0UqdOHVau\nXImTk+mjmLe3N0FBQZQrV46jR4+yZMkSevTokc0Vi73Vrl2bI0eOZHcZWWbRIj1GSkREREQkO205\nsYWOyzpmOF8fvnSYhosasrTDUhqVbZTF1dlPSkoKQwYPwWg0Urt2bZYuX2rO117eXixdvhSPih78\nfvR3li1dRrfu3bK5YrG3WrVrERkdmd1lZJlvF3yb3SWIiIiIiDzxlLGVsZ8Uytgi/y3NsC0iIiLZ\nJvxc+H0N1k5zM+kmbZa0eaRm2v711185duwYAO+//7456KZxd3enfv36AKxfv/4/r09EREREREQe\nXRHnIu7rQnKam0k36bis4yM1C9jWX7fyx7E/ABgzbozNfF23Xl0ANm7Y+J/XJyIiIiIiIo82Zex/\nKGOLiNiXBmyLiIhItkhNTaVnUM/7DrppbibdpNeqXqSmptq5sqyxdu1aAHLlykWDBg1stvHw8ADg\n6NGj/1ldIiIiIiIi8mhLTU2l75q+mcrXr6x55ZHJ1z///DNgytcvNXjJZpvKHpUB+P333/+zukRE\nREREROTRp4xtTRlbRMR+NGBbREREssW22G1EX4rO1DaiLkax/dR2O1WUtQ4ePAhApUqVcHZ2ttmm\nTZs2fPjhh7z66qsWy2NjYzEYDBgMBvOyXbt20bdvXypUqICXl1e6+71w4QLDhg2jUqVK5MqViwIF\nCtCsWTNWr15913r37dtH586dKVGiBDlz5qRw4cI899xzzJkzh8TExHT7BQcH06pVK4oWLUrOnDkp\nVqwYvr6+LF26FKPReNd9ZtTy5csxGAwUKVKEpKQkm20mTpyIwWCgdu3aVuuSk5NZvHgx9evXp1ix\nYuTMmZPixYvTuXNn8+8pK2zbtg2DwUDp0qUz1H7Xrl34+/tToEABXFxcqF27NkFBQffsd/z4cfr1\n64enpycuLi7kyZOHZ599lunTp1v9vMaPH28+twwGA9u3m15Pffr0sVju5+d3z/36+flhMBhYsGBB\nho5v+/bttGnThiJFiuDs7EyZMmV44403OHHihM32abUcOHCAhIQEJk2ahIeHBy4uLhQoUIB27dpx\n/PjxDO07o65evcqYMWOoWrUqefLkwdXVFQ8PDwYMGMDp06fT7RcfH8+0adOoWbMmefLkwcXFBU9P\nT0aOHMlff/2Vbr+4uDg++OADqlatiouLC25ubvj6+rJgwYJ0Xz+ZeX+4ePEio0ePxtvbG1dXV/Lk\nyUOtWrX47LPPSE5OzsBPSEREREQeBttPbefwpcOZ2kb0pWhCToXYqaKsFXnQ9JjeipUqppuvAwIC\nGD9hPH369rFYHhsbi7OTM85O//TbvWs3r7/6Op6VPaletXq6+71w4QIjh4/E29ObfK75KFq4KC2b\nt2TNmjV3rfe3fb/RvWt3ypUuR57ceShetDj1XqjHN3O/uWu+3hy8mbat21KyeEny5M5DKfdSNHqp\nEcuXLbdbvl65YiXOTs64F3NPN19/POljnJ2cefG5F63WJScn8/1339PQryGl3EuRJ3ceypQsQ/eu\n3Tl08JBdarRl+7btODs5U7FcxQy1371rN20C2lC0cFHy583Pi8+9yOpVd/+7CJjydf83+1OtSjXy\n581PAbcCPF/neWZ8MsPq5/XhhA/N55azkzMhIabX02uvvGaxvHGDxvfcb+MGjXF2cmbRwkUZOr6Q\n7SF0aNcB92Lu5HXJS8XyFRnQf0C6+TqtloMHDpKQkMDkjyZT1bsq+fPmp2jhonTq0ClL8vV7Y9+j\nZo2aFHArwFP5nqKqd1UGDRx0z3w9PXA6z9V+jgJuBcifNz/VqlTj3VHv3jNfT/pwEjVr1CR/3vwU\nLlCYRi81YtHCRXfN1w/6/qB8LSIiIvL4UMa2poydMcrYd6eMbT/K2I82p3s3ERERkSeZMdXI5fjL\nGI1GrsdfByDBMQEHh8zd9zVj7wx7lMeMPTPwKpz+gMQHUdClIA4G+97X9scfpkdJlSxZMt02L730\nEi+9ZPvO5TRGo5F33nmHWbNmme/MLlWqlM22ISEhtG3blsuXLwPg4ODAlStX2LRpE5s2baJ79+7M\nnz/f6tFWK1eupGPHjqSkpJj7/fXXX/z111+Ehoby008/sXnzZhwdHS36ffrppwwePNj8bwcHB86f\nP8/58+cJCQmhZ8+eLFy48K7HlxGtWrWiYMGCXLp0ifXr1xMQEGDVZtEiU+Dq08fyDwcJCQk0bdrU\nPDAZwNHRkT///JMlS5awatUqtm7dyvPPP5/pOjPjq6++YsCAAeaA4+DgwG+//Ubbtm3p3bt3uv3W\nrFlDhw4dzH+QcHBwwGg0EhYWRlhYGCEhIRaD9Z2cnCz++JKYmEhqaipOTk4Wv9+cOXPa9fhGjBjB\ntGnTzP92cHAgNjaWr7/+msWLF7N48WLatm1rs+/Nmzdp2LAhu3btAkwDuW/dusXKlSsJCwsjOjoa\nV1fXTNd4+fJlnnvuOfNrN63OI0eOcOTIEb777jtCQkKoVq2aRb+TJ0/SokULYmJiLJbHxMQQExPD\n4sWL2bp1K5UqVbJYHx0dTcuWLYmNjbU4rpCQEEJCQvjhhx9YuXIlefLkSbfm+3l/2LNnDwEBAVy6\ndMl8bEajkf3797N//35WrVrFhg0b7P67FxEREXnSpWXsVGMqt+NvA5CLXBgcDPfomb5ZobPsUtus\n0Fl4Fva0y7bSZEW+TrvIVaJEiXTb+L3kh99LfnfdjtFoZPiw4cz+bPY9Pz/vCNlBpw6drPL15uDN\nbA7eTNduXZk7b65Vvl4VtIqunbvazNf79u1j2ZJlrN+03ipffzbrM4a9M8z87zvz9Y4dO+jeozvz\n5s+76/FlREv/luZ8vWHDBlq1amXV5rvvvgOgZ++eFssTEhJo+XJL80VT+CdfL1u6jDWr1xC8JZjn\nnn8u03Vmxpyv5/D2wLct8vX+/fvp2L4jPXv1TLff2rVr6dqpq1W+Dg8LJzwsnJ07drIiaIW5fUbz\ndY6cOex6fKNHjuaT6Z+Y/+3g4MCp2FN8M+cbfvjuB+YvnE/rNq1t9r158ybNGjdj9+7dwD85dFXQ\nKiLCI4g4FGG3fF3vxXoc/+OfC9QODg4cPXKUo0eO8uP3P/LL1l+oWq2qRb+TJ0/SulVrjsQcsVh+\nJOYIR2KO8MP3P7BpyyarfH04+jCtA1pzKvaUxXHt2LGDHTt28NOPP7F0+dJ75uuMvj/s3bOX9m3b\nK1+LiIiI/MfS8jWgjJ0JytjK2PdDGVsZWxk78zTDtoiIiNzV5fjLFAksQtFPilJhbgUqzK1A0U+K\nUiSwSKa+1v6+1i71rfl9TaZr+fdXWri3p7i4OIBMfwh/7733+PTTT2nRogW//vorFy5cIDQ01Krd\n8ePH8ff35/Lly1SpUoWtW7eSlJTExYsXGTNmDA4ODnz33XcMHTrUqu+QIUNISUmhfv36REdHk5iY\nyO3bt1m3bh1ubm5s3brV6u7m+Ph4Ro8eDUC7du04ceIESUlJ3Lhxg0WLFpEjRw4WLVpERESEuU/a\nrN9pXy4uLhQtWpSiRYvi4uJisS5XrlxMnDgRMA0e7tq1K4DNAeC7d+/m2LFj5MqViy5dulismzFj\nBtu3b8fR0ZGvvvqK69evk5yczKlTp2jcuDEJCQmMGTPmPn8r9nXgwAHeeustjEYjDRo04PDhwyQn\nJ/PHH3/QpEmTdGewjo+Pp0+fPiQmJlK3bl2io6NJSkri9u3bLF26FEdHR9asWWMR9MeOHcvt27fN\nX/Xr1wdg7ty5FsuDg4PtdnyBgYHmwdpvvPEG//vf/0hOTmb//v3Uq1eP+Ph4unTpYvO8BhgwYACh\noaFMmjSJv//+m7i4OKZOnQrAqVOnzIP1M2vWrFn88ccf5MmThxUrVhAfH09ycjK///47jRo1Ii4u\njvfee8+iT0JCAs2bNycmJoZChQoxf/58rly5wu3bt9mwYQPFihXj3Llz9Oxp+QeLK1eu0KxZM2Jj\nYylZsiRr1qwhISGBq1evMmPGDHLlysXmzZvp1q3bXWvO6PvD+fPn8ff359KlS7Rv357IyEgSExOJ\ni4tj4cKFuLi48OuvvzJlypTM/yBFRERExMLl+Mu4z3CnxKclzBm7xKclcJ/h/sBfPx/72S61rTu2\nLlN12Pp6mPP1hPcn8Pmsz3m5+cts2ryJ03+eZsfuHVbtjh8/TtvWppuhvat4E7wlmJu3b3Lm3BlG\njR6Fg4MDP3z/AyOGj7DqO3zocFJSUqhXrx4HDh3gxq0bxN2MI2h1EG5ubmzbto11a9dZ9ImPj2fc\nmHEAtGnbhiPHjnDz9k3+vvY33y74lhw5cvDd4u84EHHA3CdtRrI7v9zyupkztlteN4t1H0/6GDDl\n605dOgHw3aLvrOrfs3sPfxz7g1y5ctGpcyeLdbNmziIkJARHR0c+/+JzLl+9THxCPMdOHKNho4Yk\nJCTw/rj37/O3ptjqAAAgAElEQVS3Yl8HDxxk8KDBGI1G/F7y40DkAeIT4jl89DCNGjdKd3at+Ph4\nXn/ldRITE3nxxRc5cOgAN2/fJO5mHD/89AOOjo6sW7uOHSH/nC+jx4wm7mac+atevXoAfPn1lxbL\n129cb7fj+2T6J+YLya/1e40/Tv5BfEI8e0L3ULduXeLj4+nRrQf7QvfZ7P/2oLfZt28fH3z4Aecv\nneevK3/x0eSPAFO+/m6x9TnxIGZ/NpvjfxwnT548LFm2hKvXrxKfEE9UTBQNGjYgLi6OCeMnWPRJ\nSEggwD+AIzFHKFSoEHPnzeXCXxeIuxnHmnVrzPn6ld6vWPS7cuUK/i38ORV7ipIlS7IiaAXX469z\n8fJFpk2fRq5cufhlyy/06tHrrjVn9P3hwvkLtG3dVvlaREREJBuk5Wtl7MxRxlbGzihlbGXsrM7Y\n58+ffyIytgZsi4iIiPwH4uPjAciRI3N3eX700UeMHj2atWvX8tJLL1GkSBGefvppq3YTJkwgLi6O\nkiVLEhISgp+fHw4ODhQuXJiJEycyefJkAGbPnm0xgzDA9eummdSHDh2Kp6cnjo6OODs706JFC2bO\nnMnQoUPJnz+/RZ+kpCRu3boFmAaNlilTBgcHB1xdXenRoweTJ09m6NChGAz/3NGekJBwX193Pt4m\nbebsdevWWT2eJ20Qd5s2bazqvHHjhumxXePH069fP/OdniVLluSjj0yBKb2Bwv+VWbNmkZKSgpub\nG0FBQXh4eGAwGChXrhyrV6/G3d3dZr8TJ05Qr149AgICmD9/Pp6enjg4OODs7EyHDh1o1qwZkL3H\nd+3aNSZMMAXEgQMH8uWXX1KiRAkMBgM1a9Zk8+bN1KhRg8TEREaOHGlzGwcPHmThwoW8++67PPXU\nU+TJk4fhw4fTqFEjAKKiouxSa9rroHHjxrRt25bcuXNjMBioUKECM2bMYOjQobz4ouXjyubPn8+R\nI0dwcnLi119/pXfv3uTPnx9nZ2eaNWtmft3t27ePM2fOmPvNmDGDM2fOkDdvXkJCQvD39ydHjhy4\nubkxePBg8yD0NWvWsG3btnRrzuj7Q2BgoHkG8aVLl+Lt7Y2joyN58+alZ8+ejBhh+iPYt99+m6mf\noYiIiIhIVrBXvp4yeQojRo4gaHUQfi/5pfv5edKHk8z5+petv+Dr52vO1xM+nMDEj0w3F3/1xVfp\n5uu333kbD08Pc75u3qI5gZ8EMnjIYNzc3Cz63Jmvx4wdY5Gvu3XvxsSPJjJ4yGCLfJ2YkPjA+bpX\nL9NFtfU/r7fK14sXLwYgoHWAzXzt38qfce+N47XXX7PI1x9O/BAwZZ/sNPvz2eZ8vWzFMot8vSJo\nRbr5+uSJk7xY90X8W/kzZ94cPDw9zPm6Xft2NGnaBMje47t27RqTPpwEQP+3+vP57M/N+dqnpg/r\nN62neo3qJCYmMma07RvTDx08xLz58xg5eqQ5Xw8dNpQGDRsApidB2UPa66Bho4a0btPaIl8HTje9\nDp5/wfJJZwsXLOTokaM4OTmxafMmevbqac7XTZs1Nb/ufvvtN4t8PWvmLHO+3rJ1Cy39W5rz9aC3\nB/HtAlPOXbd2Hdu3bSc9GX1/+HTGp1y+fJk6z9VRvhYRERGRR5IytjJ2RiljK2P/Vxn7cb+GrQHb\nIiIiIv8BFxcXAG7fvp1um7Nnz7J//34iIyPTbePn58ekSZPuui+j0WieAfuNN96wCnwAb7/9Nq6u\nrqSkpLB2reVs5w0amELDnDlzuHjxosW63r17ExgYyEsvvWSx3M3NjZo1awLw2Wefce3aNYv177zz\nDoGBgVSvXt28LDY2ltTUVPNXSkoKV65c4cqVK6SkpFisS01NZfz48ea+NWrUoFq1aiQlJfHjjz+a\nl6fNJg3/DOq+08SJE1m1ahVjx441L7tw4QKrV682z5ac9oeJ7JI2A3anTp3Ily+fxbpcuXIxYMAA\nm/28vb1ZtWoVq1atonz58oDpjxDh4eFMmzaNXbt2AabHMWWXLVu2cOPGDQBGjRpltd7Z2dk863tI\nSIj5UWh38vf3N8+wfqdnn30WsN/xpb0Otm3bxs6dOy3WeXt7ExgYaA6FadJeS40bN6ZKlSpW2+za\ntSuXLl3i0qVLFCtWzLw8KCgIgC5duth8/FOHDh3Mv9O0trZk5P0BYP16093me/fuxcHBAYPBYPGV\n9lqLjY21+TsQEREREclOGc3XYfvDiIpM/4ZOX19fPpj4wV33ZTQazbNzvfb6azbz9cBBA835+ud1\nljOxpT0yet7ceVb5umevnkyZNsXqsdJubm741PQB4IvZX1jl68FDBjNl2hSqVa9mXvb78d9JSE6w\n+LqdeNucsW8n3rZYN+79cea+1WtUp2q1qiQlJbHkpyXm5bdv32bFshXmWv9twocTWL5yOaPHjDYv\nu3DhAmvWrDHP5JTd+XrHDtNsTe07treZr9948w2b/by8vVi+cjnLVy63yNcR4RFMD5zOnt17AIi/\nmX3H9+svv5rz9fARw63WOzs7M3jIYMD0c7CV7Vq0bEHnLp2tlqf9fcdex5d2jodsD2HXzl0W67y8\nvZgybQrDhg+zWJ72WmrYqCHeVbytttm5S2fOnj/L2fNnLfL16tWrAejYuaPNfN2ufTvKlS9n0daW\njLw/AGzauAmA0L2hytciIiIi8khSxlbGzihlbGXsf7N7xt5gytiP+zVsDdgWERER+Q889dRTAFZ3\n0t5pxowZ1KpVi86drT/IpwkMDLS4w9eWy5cvm8Omt7f1B24wPZKpUqVKAFZ3J8+aNQsfHx9+/vln\nihYtSvny5WnVqhUTJkwgLCws3f0uXLiQcuXK8c0331CwYEE8PDxo3749U6ZM4ciRI3et+UGkDche\nsGCBednq1au5evUqJUqUoGHDhjb7nThxgnHjxuHn50fBggUpWrQorVu3Jjg42O41PoizZ88CUKFC\nBZvrPTw80u2bnJzM0qVL6datGx4eHri4uFCzZk1GjBhhvts2O504cQKAAgUK8Mwzz9hsU7VqVQBS\nU1PN7e9ka7A2mIKyPbVs2ZJhw4YRF2d6zFahQoWoV68eAwYMYPny5SQkJFj1OXnyJACenp42t+nk\n5EShQoUoVKgQjo6O5uVpx5ne6xX++bn8+/V6p4y8P4ApxKbV4+zsfNevRznsioiIiMjjKS1fX/4r\n/c+qs2bO4oXnXqB7t+7ptpk8dfJ95WtPL9uf83PmzEnFShUB06Od7/TJzE+o4VODDes3ULJ4STwq\nedC2dVsmfjCR8LDwdPc779t5lC1Xlm/nfUuxIsWo6l2Vzh07Ezg1MEvyddrF4sWLFpuXrV2z1pyv\n02aD+rcTJ04w/r3xNG7QmGJFilGyeEk6tO3Als1b7F7jg/jz7J8A5gvC/1bJo1K6fZOTk1m+bDm9\nevSiqndV8ufNz3O1n+PdUe8+Mvk67Ubi1NRUTp44abXe1oVksH++btGyBUPeGUJcXBwN/BrwzNPP\n0MC3AYMGDmLlipU283Vabk3vbyDp5eu04/Ty8kq3nrSfy/E/jqfbJiPvDwD/O/U/cz3K1yIiIiLy\nKFLGVsbOKGVsZWxb7JmxT506Za7ncc7YTtldgIiIiDzcCroU5OKwixiNRvOH5bx58+Lg8OD3fV1P\nuE7FzyuSkpqS6fqcHJw4OuAoeZ3zZnpbaQq6FLTbttJ4eXlx+vTpu4a+tMHcBQoUSLeNj4/PPfeV\nmppq/v5uH3zT2v27zTPPPENYWBihoaHs3LmT6OhowsPDWbduHePHjycgIIDly5fj5GT5UdLLy4uj\nR4+yY8cO9uzZw+HDh9m/fz8rVqxg1KhR9O/fn9mzZ9+z/ozq1q0bI0aMIDw8nKioKLy9vVm4cCFg\nmgnc1jk6d+5cBgwYQFJSEp6enrRs2ZLKlStTo0YNSpQocdcBs/+VtN/LnWHoTuktv3z5Mk2bNiUs\nLIw8efJQt25dmjdvjqenJy+++CKTJ082/3yyS3rnnK026bUrV66c/QtLx7Rp0xgyZAjBwcFERkZy\n6NAhFi1axBdffEHx4sVZt26dxazxmfUgr9c7ZeT94U4zZ85Md8Z2EREREckaBV0KcmbIGVKNqdyO\nN81elcslFwaHe1+0sOV6wnW8v/S2W76OfCPyoc/XHp4enD59mqNHj6bbJu2iTYGn0s/XNXxq3HNf\n9sjXe/ftZV/oPnbt2kXM4RgiIiJY//N6PvzgQ/xb+fPT0p+s8rWnlydRh6PYuWMnoXtDiYmJISws\njKCVQYx5dwz93uzHrM9m3bP+jOrStQvvjnqXiPAIoqOi8fL2Ml9Y7tGzh818Pe+bebw98G2SkpLw\n8PSgeYvmVKpUiWrVq1GiRAlqVLv3zzer3TNfO6Sfr1s2b0l4WDh58uThhRdfoFmzZnh4evD8C88T\nODXQ4sJ7drBHvi5btqz9C0vH5KmTGTR4EFs2byEqMorIyEi+X/w9X3/5NcWLFydodZDFjHaZldl8\nnZH3hzt9MuMTBr418L76iIiIiEjmpOVrQBk7E5SxlbEzShlbGduWrMjYM2bM4K233rqvPo8SDdgW\nERGRu3IwOFDYtTBGoxHnFNPdf/lc82VqwHZh18K08WjD8sPLM11fm8ptKFvgv/vw+6Dq1avHxo0b\nOXfuHNHR0TbvQoyIiADuPstuRhQqVAg3NzeuXbtGZGQkLVu2tGqTmJjI77//DqQ/ALZOnTrUqVPH\n/O+jR4/SpEkTVq9ezYwZMxg+3PpxQI6Ojvj5+eHn52deFhoaStOmTfniiy/w8/OjQ4cOmTq+NIUK\nFaJly5asXLmSBQsWMGzYMIKDgzEYDPTu3duq/enTp82DtT///HOrgappd5Zmt6JFi3Lq1Cmru8bT\npPcHkzFjxhAWFkbFihXZu3ev+Y74h0nauXb58mX+/PNPm3coR0ZGAqZQV6ZMGav1OXLkyNoi/+WZ\nZ56xOJ9u3brFgAEDmD9/Pt26dSM6Otq8rkyZMsTExBATE5Pu9jw8PLh+/TqjR482n4Nly5YlKirK\nfOy2REWZHjNnjwHrpUuXJiYmhmPHjtlcn5iYyKFDhwCoWLGi1WPNREREROTBpWXsVGMqt7gFQG7X\n3A98Mbmwa2ECKgewMmZlpmsLqBTwSOTrunXrErwpmHPnznE4+rDNWbkOHDgAgKe37Rm7MurOfB0d\nFU2Lli2s2iQmJnLsd9Nn6/QuztWuU5vadWqb/3306FFavtyStWvW8unMTxk6bKhVH0dHR3z9fPH1\n8zUv2xe6j5bNW/L1l1/j6+tLu/btMnV8aQoVKkTzFs1ZFbSKRQsXMWToELZs3oLBYKBHrx5W7U+f\nPm2+kDxz1kze7P+mxfqHLV/benoTYP67yL+9N+49wsPCqVCxAjt27Xhk83VajjQYDJQuU9pqfXbk\n6zsf/X3r1i3eHvg2CxcspFfPXhw4dMC8rnTp0hyJOXLXiQ+qelflxvUbDB853HwOlilbhuioaPOx\n25KW48uWy/z7XclSJTl65Ch/HLP9NCzlaxEREZGsk5avAWXsTFDGVsbOKGVsZWxb7JmxS5UqxZEj\nRx77a9gPPtJKREREJBP6P9vfPtupZZ/tZLWePXua7zadMGGC1fq9e/eaP1w2bdo0U/tycHCgVatW\nAHz99dfmR0vd6bPPPuPmzZs4Ojqa2wKsXr2a/PnzU7NmTas+lSpVonXr1gCEhIRYbS9//vy0adPG\nql+dOnXw9fW12S+z+vTpA8D333/PggULSElJoX79+jYD/O7du0lKSgJMs3P/28cff2zX2h5U3bp1\nAViyZAk3b960WJeYmMgXX3xhs9/27dsBePnll62C7pEjR1i1atU99507d24Am+eMPTRq1Ii8eU0z\nCUydOtVqfWJiIjNmzACgfv36FCxo/5kCMqp48eLkz5+f0NBQi+W5c+dm4EDTrFmHDx+2eNxS2s0R\nwcHBFgO504SFhXHkyBHOnj1LyZIlzcvTXjc//vgj//vf/6z6rVixwhxMbb3G7lf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f9V/rPOW1f/r2Od46oJ6CTu/VPLtLhl+q014LSEZQo/+7LhBLavvnrwGquI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\ndWzd1Le/o8x9DzTWW+7a3qiZ/8BNfb1OL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      "text/plain": [
       "<Figure size 3600x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画出学习曲线\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "cv=ShuffleSplit(n_splits=10,test_size=0.2,random_state=0)\n",
    "plt.figure(figsize=(18,4),dpi=200)\n",
    "title='Learning Curves (degree={0})' \n",
    "degrees=[1,2,3]\n",
    "\n",
    "start=time.clock()\n",
    "plt.figure(figsize=(18,4),dpi=200)\n",
    "for i in range(len(degrees)):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    plot_learning_curve(plt,polynomial_model(degrees[i]),title.format(degrees[i]),X,Y,ylim=(0.01,1.01),cv=cv)\n",
    "    print('elaspe:{0:.6f}'.format(time.clock()-start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 图一 随着样本的增加，validation score和training score都在一个比较低的水平，则此时为欠拟合，再增加样本也无法增加模型的score。此时整个模型没有很好的抓住数据的特征。\n",
    "### 图二 在最大样本数量的地方，training score比validation score大很多，属于过拟合。使用了过多特征\n",
    "### 图三 模型偏差较大。\n",
    "#### 综上上述三个模型都有点欠缺，整体不是很好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
